Communities of Practice

Staff Collective for Data Science

The Staff Collective for Data Science (SCDS) is a community of University of Michigan staff who are active or interested in data science and AI. We aim to advance research innovation on campus, augment the group’s expertise, and support the research and career endeavors of our members. All SCDS members together drive the goals, activities and impact of the group.

Explore the Collective

Ideas Hub for Research

MIDAS Working Groups

MIDAS coordinates efforts to bring attention to important research topics that cut across traditional disciplines, fosters interaction between theorists and application scientists, enables innovative ideas and new collaboration, and elevates the quality of data science research across U-M campuses.  We particularly encourage researcher-initiated working groups and workshops to:

  • Identify novel research themes where U-M researchers have the potential of making significant scientific contribution and societal impact;
  • Develop research ideas that will become major grant proposals;
  • Build interdisciplinary teams.

For researcher-initiated working groups, MIDAS can help coordinate the activities and connect researchers from all U-M units with diverse backgrounds and expertise.

To join a current working group and submit an idea for a new group, please email midas-research@umich.edu.

Current working groups:

Social Research with Unstructured Data

This group hosts a connection series to present works in progress, funding opportunities, and new data sources to facilitate collaborations between social scientists and data scientists.

Sequential Decision Making

This group is exploring current and developing SDM methodologies and their application in health care as well as other areas, and seeking large grant opportunities to support major methodological development.

Past Working Groups

  • Data Integration.  Challenges such as idiosyncratic integration methods, missing data, bias and coverage, consistency and quality control issues.
  • Data Science for Music.  This group attracted researchers with diverse backgrounds to discuss research at the intersection of data science and music.
  • Learning Environment in the Time of COVID-19.  This group discussed data science methods to design an inclusive, innovative, and resilient university from three angles: how to plan for the fall of 2020, building adaptive capacity for 2021, and improving the university for the long term using evidence-driven strategies.
  • Mobile Sensor Analytics. Discussions on theory and application in mobile sensor analytics, including real-time data collection, streaming data analysis, active on-line learning, mobile sensor networks, and energy efficient mobile computing.
  • Multi-scale Integration in Biology.  This group facilitated the dialogue between biologists / biomedical researchers and statisticians / mathematicians to enable biology-inspired math and statistics research on multi-scale integration.
  • Selection Bias and Missing Data in COVID-19 Population Studies.  This group focused on core information for data collection; datasets on sensitivity and specificity; methodology and tools.
  • Teaching Data Science. Discussions on issues such as: How do we teach data science to students with various levels of preparation?  How do we build data science modules to incorporate into existing domain science courses?  How do we raise awareness of ethics and social responsibility in data science teaching?  How do we teach data science to independent researchers?
  • Trustworthy Data Science. Data security, privacy, data fairness, validity, and sensible applications to policy.  

Past working groups linked with funding opportunities:

  • Foundation Funding and Social Science
  • NSF Big Data Spokes
  • NSF BIGDATA
  • NSF Resource Implementations for Data Intensive Research in the Social, Behavioral and Economic Sciences
  • NSF Secure and Trustworthy Cyberspace